A Systematic Review Protocol to Determine the Most Effective Strategies to Reduce Computed Tomography Usage in the Emergency Department
Bibliographic record
Abstract
This study describes the protocol for a systematic review and meta-analysis. The primary objective of the review is to identify experimental studies assessing the effectiveness of interventions that aim to reduce the proportion of computed tomography (CT) in emergency departments (EDs). Data permitting, our secondary objectives will be to assess the impact of reduction in CT utilization on the length of stay, admission to hospital, and uptake/satisfaction with the intervention. When available, balancing measures such as readmission to hospital or ED revisit rates will be included. Pre-defined subgroup analyses include patient populations (adult or pediatric), type of ED, and the nature of the intervention. Through this review, the research team aims to inform knowledge translation initiatives aimed at lowering CT usage in the ED by identifying the most effective interventions to safely improve CT resource stewardship.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.092 | 0.115 |
| Meta-epidemiology (narrow) | 0.006 | 0.005 |
| Meta-epidemiology (broad) | 0.015 | 0.017 |
| Bibliometrics | 0.012 | 0.013 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.119 | 0.019 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".